========================================================================= 
Log Path: ./log/replication.log 
Program Path: /Users/beatricemagistro/PEARL Dropbox/LR Research Lab - Shared/Papers/automation_geography/Study3_Tradeoffs/Replication/models_main_paper_log.R 
Working Directory: /Users/beatricemagistro/PEARL Dropbox/LR Research Lab - Shared/Papers/automation_geography/Study3_Tradeoffs/Replication 
User Name: beatricemagistro 
R Version: 4.2.2 (2022-10-31) 
Machine: MacBook-Pro-2.local x86_64 
Operating System: Darwin 23.2.0 Darwin Kernel Version 23.2.0: Wed Nov 15 21:54:51 PST 2023; root:xnu-10002.61.3~2/RELEASE_ARM64_T6030 
Base Packages: grid stats graphics grDevices utils datasets methods base Other Packages: logr_1.3.4 gt_0.9.0 gtsummary_1.7.1 ggpubr_0.6.0 rstatix_0.7.2 lubridate_1.9.2 forcats_1.0.0 tibble_3.2.1 tidyverse_2.0.0 clusterSEs_2.6.5 Formula_1.2-5 AER_1.2-10 cjoint_2.1.0 survey_4.2-1 survival_3.4-0 cregg_0.4.0 fixest_0.11.0 texreg_1.38.6 clubSandwich_0.5.8 ggeffects_1.1.4 sjmisc_2.8.9 sjlabelled_1.2.0 sjPlot_2.8.12 jtools_2.2.1 brms_2.18.0 Rcpp_1.0.10 haven_2.5.1 readr_2.1.4 modelr_0.1.10 multiwayvcov_1.2.3 estimatr_1.0.0 plm_2.6-2 stringr_1.5.0 xtable_1.8-4 car_3.1-1 carData_3.0-5 lattice_0.20-45 foreign_0.8-84 arm_1.13-1 lme4_1.1-31 Matrix_1.5-1 MASS_7.3-58.1 plyr_1.8.8 lmtest_0.9-40 zoo_1.8-11 sandwich_3.0-2 expss_0.11.4 maditr_0.8.3 purrr_1.0.1 broom_1.0.3 stargazer_5.2.3 marginaleffects_0.12.0.9009 magrittr_2.0.3 kableExtra_1.3.4 knitr_1.41 modelsummary_1.3.0 ggstance_0.3.6 gridExtra_2.3 ggplot2_3.4.2 dplyr_1.1.2 tidyr_1.3.0 
Log Start Time: 2023-12-19 01:30:43 
========================================================================= 

Model DV fairness 

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Call:
lm(formula = agree_1 ~ group, data = multi)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.6811 -0.6239  0.1261  0.6261  1.7695 

Coefficients:
                          Estimate Std. Error t value             Pr(>|t|)    
(Intercept)                3.23052    0.02636  122.57 < 0.0000000000000002 ***
groupSpecific Information  0.14337    0.02938    4.88           0.00000108 ***
groupGeneric Information   0.45055    0.03707   12.15 < 0.0000000000000002 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.93 on 7652 degrees of freedom
  (378 observations deleted due to missingness)
Multiple R-squared:  0.02069,	Adjusted R-squared:  0.02043 
F-statistic: 80.83 on 2 and 7652 DF,  p-value: < 0.00000000000000022


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Predicted values model DV fairness 

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# Predicted values of agree_1

Predicted |       95% CI
------------------------
     3.23 | [3.18, 3.28]
     3.37 | [3.35, 3.40]
     3.68 | [3.63, 3.73]

NOTE: Data frame has 3 rows and 6 columns. 

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Figure 1 

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<ggproto object: Class CoordFlip, CoordCartesian, Coord, gg>
    aspect: function
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    clip: on
    default: FALSE
    distance: function
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    is_free: function
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    setup_panel_params: function
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    super:  <ggproto object: Class CoordFlip, CoordCartesian, Coord, gg>

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Summary of policies 

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# A tibble: 24 × 3
   group                policy_option support
   <fct>                <fct>           <dbl>
 1 News                 socialsupport    3.66
 2 Specific Information socialsupport    3.67
 3 Generic Information  socialsupport    3.63
 4 News                 basicincome      3.54
 5 Specific Information basicincome      3.49
 6 Generic Information  basicincome      3.46
 7 News                 jobguarantee     3.49
 8 Specific Information jobguarantee     3.51
 9 Generic Information  jobguarantee     3.47
10 News                 unskilled        3.57
# ℹ 14 more rows
# ℹ Use `print(n = ...)` to see more rows

NOTE: Data frame has 24 rows and 3 columns. 

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Figure 2 

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<ggproto object: Class ScaleDiscretePosition, ScaleDiscrete, Scale, gg>
    aesthetics: x xmin xmax xend
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    break_positions: function
    breaks: waiver
    call: call
    clone: function
    dimension: function
    drop: TRUE
    expand: waiver
    get_breaks: function
    get_breaks_minor: function
    get_labels: function
    get_limits: function
    guide: waiver
    is_discrete: function
    is_empty: function
    labels: Social support Basic Income Job Guarantee Unskilled Skil ...
    limits: NULL
    make_sec_title: function
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    na.translate: TRUE
    na.value: NA
    name: waiver
    palette: function
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    position: bottom
    range: environment
    range_c: environment
    rescale: function
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    scale_name: position_d
    train: function
    train_df: function
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    transform_df: function
    super:  <ggproto object: Class ScaleDiscretePosition, ScaleDiscrete, Scale, gg>

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Figure 3 

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Marginal means 

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      outcome statistic                        feature       level estimate  std.error        z p
1  scenagree1        mm Number of High Skilled - After         200 3.319682 0.01738527 190.9479 0
2  scenagree1        mm Number of High Skilled - After         250 3.406216 0.01677745 203.0235 0
3  scenagree1        mm Number of High Skilled - After         350 3.427936 0.01634601 209.7109 0
4  scenagree1        mm  Number of Low Skilled - After         150 3.426191 0.01454813 235.5074 0
5  scenagree1        mm  Number of Low Skilled - After          50 3.343826 0.01516423 220.5075 0
6  scenagree1        mm   Wage of High Skilled - After    $125,000 3.389576 0.01489541 227.5584 0
7  scenagree1        mm   Wage of High Skilled - After    $150,000 3.380286 0.01466662 230.4748 0
8  scenagree1        mm    Wage of Low Skilled - After     $20,000 3.345414 0.01511083 221.3918 0
9  scenagree1        mm    Wage of Low Skilled - After     $25,000 3.424581 0.01444442 237.0868 0
10 scenagree1        mm                  Price - After  Same price 3.190380 0.01799769 177.2660 0
11 scenagree1        mm                  Price - After 20% cheaper 3.435280 0.01672355 205.4158 0
12 scenagree1        mm                  Price - After 50% cheaper 3.520337 0.01624181 216.7454 0
13 scenagree1        mm                        Product         Car 3.402234 0.01825524 186.3703 0
14 scenagree1        mm                        Product       Plane 3.368293 0.01853386 181.7373 0
15 scenagree1        mm                        Product  Smartphone 3.394149 0.01867539 181.7444 0
16 scenagree1        mm                        Product     Vaccine 3.375127 0.01886769 178.8839 0
      lower    upper
1  3.285608 3.353757
2  3.373333 3.439099
3  3.395898 3.459974
4  3.397677 3.454705
5  3.314105 3.373547
6  3.360382 3.418771
7  3.351540 3.409032
8  3.315797 3.375031
9  3.396270 3.452892
10 3.155105 3.225654
11 3.402502 3.468057
12 3.488504 3.552171
13 3.366454 3.438013
14 3.331967 3.404619
15 3.357546 3.430752
16 3.338147 3.412107

NOTE: Data frame has 16 rows and 10 columns. 

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Figure 4 

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AMCE 

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      outcome statistic                        feature       level     estimate  std.error          z
1  scenagree1      amce Number of High Skilled - After         200  0.000000000         NA         NA
2  scenagree1      amce Number of High Skilled - After         250  0.083186176 0.01955735  4.2534472
3  scenagree1      amce Number of High Skilled - After         350  0.104401216 0.01960937  5.3240464
4  scenagree1      amce  Number of Low Skilled - After         150  0.000000000         NA         NA
5  scenagree1      amce  Number of Low Skilled - After          50 -0.083204367 0.01608370 -5.1732100
6  scenagree1      amce   Wage of High Skilled - After    $125,000  0.000000000         NA         NA
7  scenagree1      amce   Wage of High Skilled - After    $150,000 -0.008216356 0.01581288 -0.5195991
8  scenagree1      amce    Wage of Low Skilled - After     $20,000  0.000000000         NA         NA
9  scenagree1      amce    Wage of Low Skilled - After     $25,000  0.078917473 0.01589745  4.9641579
10 scenagree1      amce                  Price - After  Same price  0.000000000         NA         NA
11 scenagree1      amce                  Price - After 20% cheaper  0.243988807 0.02045269 11.9294212
12 scenagree1      amce                  Price - After 50% cheaper  0.330080980 0.02079575 15.8725244
13 scenagree1      amce                        Product         Car  0.000000000         NA         NA
14 scenagree1      amce                        Product       Plane -0.037445554 0.02212682 -1.6923150
15 scenagree1      amce                        Product  Smartphone -0.006121182 0.02217115 -0.2760877
16 scenagree1      amce                        Product     Vaccine -0.023316745 0.02223105 -1.0488369
                                                                   p       lower        upper
1                                                                 NA          NA           NA
2  0.000021050461183384169036158367616096143137838225811719894409180  0.04485446  0.121517887
3  0.000000101483989266413140797759790193155460258367384085431694984  0.06596755  0.142834882
4                                                                 NA          NA           NA
5  0.000000230106186584308680766872601437811329816440775175578892231 -0.11472784 -0.051680891
6                                                                 NA          NA           NA
7  0.603343029758516058436157436517532914876937866210937500000000000 -0.03920903  0.022776313
8                                                                 NA          NA           NA
9  0.000000689997752388787655547757859259005996932501147966831922531  0.04775904  0.110075910
10                                                                NA          NA           NA
11 0.000000000000000000000000000000008314965554585319724294360852119  0.20390226  0.284075351
12 0.000000000000000000000000000000000000000000000000000000009821002  0.28932207  0.370839893
13                                                                NA          NA           NA
14 0.090585925314757800852483171638596104457974433898925781250000000 -0.08081333  0.005922221
15 0.782480703930576337867819347593467682600021362304687500000000000 -0.04957584  0.037333472
16 0.294253193388028522203825332326232455670833587646484375000000000 -0.06688880  0.020255312

NOTE: Data frame has 16 rows and 10 columns. 

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AMCE objective 

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           BY    outcome       statistic                        feature       level   scenknow_all
1  High - Low scenagree1 amce_difference                        college  No College High Knowledge
2  High - Low scenagree1 amce_difference Number of High Skilled - After         250 High Knowledge
3  High - Low scenagree1 amce_difference Number of High Skilled - After         350 High Knowledge
4  High - Low scenagree1 amce_difference  Number of Low Skilled - After          50 High Knowledge
5  High - Low scenagree1 amce_difference                  Price - After 20% cheaper High Knowledge
6  High - Low scenagree1 amce_difference                  Price - After 50% cheaper High Knowledge
7  High - Low scenagree1 amce_difference                        Product       Plane High Knowledge
8  High - Low scenagree1 amce_difference                        Product  Smartphone High Knowledge
9  High - Low scenagree1 amce_difference                        Product     Vaccine High Knowledge
10 High - Low scenagree1 amce_difference   Wage of High Skilled - After    $150,000 High Knowledge
11 High - Low scenagree1 amce_difference    Wage of Low Skilled - After     $25,000 High Knowledge
      estimate  std.error          z                p       lower      upper
1   0.08732285 0.06961127  1.2544355 0.20968374742058 -0.04911273 0.22375843
2   0.11420527 0.08249384  1.3844096 0.16623306248594 -0.04747969 0.27589024
3   0.19172978 0.08252817  2.3232040 0.02016819445656  0.02997754 0.35348203
4  -0.08997417 0.06678106 -1.3473008 0.17788336676589 -0.22086264 0.04091429
5   0.45048491 0.08236538  5.4693475 0.00000004516954  0.28905172 0.61191810
6   0.44376350 0.08163342  5.4360515 0.00000005447420  0.28376493 0.60376207
7   0.01381158 0.09397303  0.1469739 0.88315265432873 -0.17037217 0.19799532
8  -0.08570517 0.09510135 -0.9011983 0.36748291245903 -0.27210040 0.10069005
9   0.07179219 0.09544394  0.7521922 0.45193546077532 -0.11527450 0.25885888
10  0.09329270 0.06677809  1.3970555 0.16239687576116 -0.03758995 0.22417534
11 -0.04322863 0.06697100 -0.6454828 0.51861432902656 -0.17448938 0.08803211

NOTE: Data frame has 11 rows and 12 columns. 

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Figure 5 

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AMCE subjective 

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           BY    outcome       statistic                        feature       level
1  High - Low scenagree1 amce_difference Number of High Skilled - After         250
2  High - Low scenagree1 amce_difference Number of High Skilled - After         350
3  High - Low scenagree1 amce_difference  Number of Low Skilled - After          50
4  High - Low scenagree1 amce_difference                  Price - After 20% cheaper
5  High - Low scenagree1 amce_difference                  Price - After 50% cheaper
6  High - Low scenagree1 amce_difference                        Product       Plane
7  High - Low scenagree1 amce_difference                        Product  Smartphone
8  High - Low scenagree1 amce_difference                        Product     Vaccine
9  High - Low scenagree1 amce_difference   Wage of High Skilled - After    $150,000
10 High - Low scenagree1 amce_difference    Wage of Low Skilled - After     $25,000
   self_know_automation_cat   estimate  std.error         z             p        lower     upper
1            High Self-Know 0.05470453 0.05942486 0.9205664 0.35727683350 -0.061766058 0.1711751
2            High Self-Know 0.11974633 0.06016655 1.9902477 0.04656365732  0.001822066 0.2376706
3            High Self-Know 0.00736658 0.04987939 0.1476879 0.88258911945 -0.090395227 0.1051284
4            High Self-Know 0.11810516 0.06351006 1.8596293 0.06293798633 -0.006372259 0.2425826
5            High Self-Know 0.26209851 0.06450225 4.0634008 0.00004836287  0.135676416 0.3885206
6            High Self-Know 0.08057916 0.06761070 1.1918107 0.23333547550 -0.051935383 0.2130937
7            High Self-Know 0.07217827 0.06862381 1.0517962 0.29289304878 -0.062321934 0.2066785
8            High Self-Know 0.03678038 0.06734377 0.5461586 0.58495694866 -0.095210990 0.1687717
9            High Self-Know 0.02179091 0.05010306 0.4349218 0.66361921097 -0.076409288 0.1199911
10           High Self-Know 0.04334043 0.04877185 0.8886362 0.37419861328 -0.052250632 0.1389315

NOTE: Data frame has 10 rows and 12 columns. 

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Figure 6 

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========================================================================= 
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========================================================================= 
